def test_1():
# Test the getters and setters
t1 = np.random.randn(10, 2)
n_features = 2
for n_states in [3, 4]:
hmm = GaussianHMMCPUImpl(n_states, n_features)
hmm._sequences = [t1]
means = np.random.randn(n_states, n_features).astype(np.float32)
hmm.means_ = means
yield lambda: np.testing.assert_array_almost_equal(hmm.means_, means)
vars = np.random.rand(n_states, n_features).astype(np.float32)
hmm.vars_ = vars
yield lambda: np.testing.assert_array_almost_equal(hmm.vars_, vars)
transmat = np.random.rand(n_states, n_states).astype(np.float32)
hmm.transmat_ = transmat
yield lambda: np.testing.assert_array_almost_equal(
hmm.transmat_, transmat)
startprob = np.random.rand(n_states).astype(np.float32)
hmm.startprob_ = startprob
yield lambda: np.testing.assert_array_almost_equal(
hmm.startprob_, startprob)
def test_2():
n_features = 3
length = 32
for n_states in [4]:
t1 = np.random.randn(length, n_features)
means = np.random.randn(n_states, n_features)
vars = np.random.rand(n_states, n_features)
transmat = np.random.rand(n_states, n_states)
transmat = transmat / np.sum(transmat, axis=1)[:, None]
startprob = np.random.rand(n_states)
startprob = startprob / np.sum(startprob)
chmm = GaussianHMMCPUImpl(n_states, n_features)
chmm._sequences = [t1]
pyhmm = GaussianHMM(n_components=n_states,
init_params='',
params='',
covariance_type='diag')
chmm.means_ = means.astype(np.float32)
chmm.vars_ = vars.astype(np.float32)
chmm.transmat_ = transmat.astype(np.float32)
chmm.startprob_ = startprob.astype(np.float32)
clogprob, cstats = chmm.do_estep()
pyhmm.means_ = means
pyhmm.covars_ = vars
pyhmm.transmat_ = transmat
pyhmm.startprob_ = startprob
framelogprob = pyhmm._compute_log_likelihood(t1)
fwdlattice = pyhmm._do_forward_pass(framelogprob)[1]
bwdlattice = pyhmm._do_backward_pass(framelogprob)
gamma = fwdlattice + bwdlattice
posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T
stats = pyhmm._initialize_sufficient_statistics()
pyhmm._accumulate_sufficient_statistics(stats, t1, framelogprob,
posteriors, fwdlattice,
bwdlattice, 'stmc')
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], cstats['trans'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['post'], cstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs'], cstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs**2'], cstats['obs**2'], decimal=3)
def test_2():
n_features = 3
length = 32
for n_states in [4]:
t1 = np.random.randn(length, n_features)
means = np.random.randn(n_states, n_features)
vars = np.random.rand(n_states, n_features)
transmat = np.random.rand(n_states, n_states)
transmat = transmat / np.sum(transmat, axis=1)[:, None]
startprob = np.random.rand(n_states)
startprob = startprob / np.sum(startprob)
chmm = GaussianHMMCPUImpl(n_states, n_features)
chmm._sequences = [t1]
pyhmm = GaussianHMM(n_components=n_states, init_params='', params='', covariance_type='diag')
chmm.means_ = means.astype(np.float32)
chmm.vars_ = vars.astype(np.float32)
chmm.transmat_ = transmat.astype(np.float32)
chmm.startprob_ = startprob.astype(np.float32)
clogprob, cstats = chmm.do_estep()
pyhmm.means_ = means
pyhmm.covars_ = vars
pyhmm.transmat_ = transmat
pyhmm.startprob_ = startprob
framelogprob = pyhmm._compute_log_likelihood(t1)
fwdlattice = pyhmm._do_forward_pass(framelogprob)[1]
bwdlattice = pyhmm._do_backward_pass(framelogprob)
gamma = fwdlattice + bwdlattice
posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T
stats = pyhmm._initialize_sufficient_statistics()
pyhmm._accumulate_sufficient_statistics(
stats, t1, framelogprob, posteriors, fwdlattice,
bwdlattice, 'stmc')
yield lambda: np.testing.assert_array_almost_equal(stats['trans'], cstats['trans'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post'], cstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'], cstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs**2'], cstats['obs**2'], decimal=3)
def test_1():
# Test the getters and setters
t1 = np.random.randn(10, 2)
n_features = 2
for n_states in [3, 4]:
hmm = GaussianHMMCPUImpl(n_states, n_features)
hmm._sequences = [t1]
means = np.random.randn(n_states, n_features).astype(np.float32)
hmm.means_ = means
yield lambda: np.testing.assert_array_almost_equal(hmm.means_, means)
vars = np.random.rand(n_states, n_features).astype(np.float32)
hmm.vars_ = vars
yield lambda: np.testing.assert_array_almost_equal(hmm.vars_, vars)
transmat = np.random.rand(n_states, n_states).astype(np.float32)
hmm.transmat_ = transmat
yield lambda: np.testing.assert_array_almost_equal(hmm.transmat_, transmat)
startprob = np.random.rand(n_states).astype(np.float32)
hmm.startprob_ = startprob
yield lambda: np.testing.assert_array_almost_equal(hmm.startprob_, startprob)
